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Predicting Innovative Growth and Demand with Proximate Human Capital: a Case Study of the Helsinki Metropolitan Area

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Predicting Innovative Growth and Demand with Proximate Human Capital: a Case Study of the Helsinki Metropolitan Area Cities 64 (2017) 9–17 Contents lists available at ScienceDirect Cities journal homepage: www.elsevier.com/locate/cities Predicting innovative growth and demand with proximate human capital: A case study of the Helsinki metropolitan area Juho Kiuru a,⁎, Tommi Inkinen b a University of Helsinki, Department of Geosciences and Geography, Division of Urban Geography and Regional Studies, P.O. Box 64, (Gustaf Hällströmin katu 2a), FI-00014 University of Helsinki, Finland b University of Turku, Centre for Maritime Studies, Brahea-Centre, FI-20014 University of Turku, Finland article info abstract Article history: Human capital is an essential driver for the growth of national and regional innovation systems. In this study, we Received 17 August 2016 can show that also intra-metropolitan innovation clusters locate in, or in proximity to, neighbourhoods with a Received in revised form 4 January 2017 high level of human capital. Our interpretation of human capital involves an educated, talented, creative and Accepted 16 January 2017 tolerant workforce. Indicators from earlier literature are complemented by identified new propositions. In addi- Available online xxxx tion, by using both relative and absolute measures, we conclude that innovations emerge the best in dense and mixed urban structure. After identifying the geography of human capital and innovativeness, we predict also Keywords: Urban planning potential growth areas, which could help urban planning of the HMA. The modelling methods used in this Urban geography study can be implemented and applied in urban studies of other city regions. Human capital © 2017 Elsevier Ltd. All rights reserved. Innovation Predictive analytics 1. Introduction World Economic Forum, 2015). This article presents a statistical analysis of the intra-regional characteristics and interdependencies within the Numerous studies have proven that one of the key drivers for HMA. It will also produce applicable research results for strategic economic growth is human capital. This refers to workforce characteris- planning. We will conduct a predictive analysis on the potential growth tics, including indicators such as the level of education (tertiary), locations based on postal code data. employment knowledge intensity, and employee know-how (e.g. Our research questions are: Glaeser & Saiz, 2004; Alquézar Sabadie & Johansen, 2010). Other studies have applied the concept of creative class, including indicators of talent 1) Do intra-metropolitan clusters of innovation locate in proximity to and tolerance of the workforce (Florida, 2002, 2012). Some studies have neighbourhoods with a high level of human capital? recognised the correlation between human capital and innovativeness 2) How can we predict potential growth areas to help the urban plan- in the national context (Barro, 1991; Rauch, 1993; Simon & Nardinelli, ning of the area? 1996; Simon, 1998). Similar approaches have also been used in regional These research problems relate to both the international context and and urban studies (Zucker, Darby, & Brewer, 1997; Glaeser, 2000; Henry local urban planning. By discovering which indicators are relevant to & Pinch, 2000; Florida, 2002; Florida, Mellander, & Stolarick, 2008; intra-metropolitan innovation clusters, and which indicators of human Lawton Smith, 2009; Boschma & Fritch, 2009; Doms, Lewis, & Robb, capital predict local scale innovativeness, this study contributes to the 2010; Glaeser, Kerr, & Ponzetto, 2010). However, the connection international debate concerning innovation clustering, and especially between human capital (individual capabilities) and spatial innovation to human capital driven growth. clusters (firm properties) has not been greatly studied on an intra- The main findings of our study visualise and statistically verify that metropolitan scale. intra-metropolitan clustering of innovation and human capital hotspots Our study uses the Helsinki Metropolitan Area (HMA) that is the are closely related, not only statistically but also spatially. Our study also capital area and the most important economic concentration in provides indications of areas that could benefit from proximity to these Finland (Makkonen & Inkinen, 2015) as the case location. Finland has innovation hotspots. Another main result is that our analysis illustrates been deemed one of the most innovative countries in the world (e.g. (with solid, diverse, and independently collected datasets) degrees and volumes of distribution in terms of innovation and human capital indi- cators (spatial clustering presented in Fig. 1). In terms of practical sug- ⁎ Corresponding author. gestions, our spatial lag model results provide insights into new E-mail addresses: juho.kiuru@helsinki.fi (J. Kiuru), tommi.inkinen@utu.fi (T. Inkinen). development actions, and bring forth new areas that could have http://dx.doi.org/10.1016/j.cities.2017.01.005 0264-2751/© 2017 Elsevier Ltd. All rights reserved. 10 J. Kiuru, T. InkinenCities 64 (2017) 9–17 Fig. 1. Geographical distribution of human capital and innovation scores in the HMA. potential for intra-urban strategic planning. The models used in this Education has been widely studied in urban and regional studies, and study can also be used and implemented in empirical urban research it is deemed to be one of the most important societal indicators in other city regions that have similar data resources available. depicting potential and actualised levels of innovation at a location (e.g. Doms et al., 2010; Glaeser et al., 2010; Makkonen & Inkinen, 2. Literature based background 2013). Additionally, the workforce age structure is significant. This re- fers to the number of skilled workers in the age groups close to Human capital has traditionally been considered important in the 40 years (e.g. Bönte, Falck, & Heblich, 2009; Glaeser & Kerr, 2009). (innovative) economic growth of nations and regions, including the Other recognised measures include the number of creative occupations widely applied Porter's diamond approach (e.g. Barro, 1991; Rauch, (e.g. Florida, 2002; Marlet & Van Woerkens, 2004; Mellander & Florida, 1993; Simon & Nardinelli, 1996; Simon, 1998; Porter, 2000) and regions 2006; Boschma & Fritch, 2009; Florida, 2012) and the proportion of (e.g. Zucker et al., 1997; Simon, 1998; Glaeser, 2000; Henry & Pinch, skilled professional immigrant workforce (Stephan & Levin, 2001; 2000; Florida, 2002; Simon & Nardinelli, 2002; Glaeser & Saiz, 2004; Florida, 2002). In this study, we will measure creative occupations Florida et al., 2008; Lawton Smith, 2009; Boschma & Fritch, 2009; with the Standard Industrial Classification (SIC). Doms et al., 2010; Glaeser et al., 2010; Florida, 2012). Fu (2007) demon- From single indicators, inadequate figures for artists, immigrants strated that human capital externalities work locally, even at the census and gay people have been criticised in several studies (e.g. Clark, block level in the Boston Metropolitan Area. In addition, the knowledge 2003; Glaeser, 2004). In this study, different indicators of human capital, spill overs were evident only in the most central census blocks. Fu including the aspect of tolerance, are tested. Instead of the number of named the concept Smart Café Cities. However, in Fu's study, spillover immigrants, we measure areal tolerance, using the popularity of effects were tested between workforce features. Our goal is to deepen immigration-critical political parties as a gauge. We believe that a low the spatial focus of the relationship between human capital and innova- popularity of immigration-critical political parties sufficiently reflects tion. This will also help to bridge the human capital and innovation clus- the tolerance of different areas. We bring a new indicator of workforce tering from regional studies towards urban studies. tolerance into the analysis of human capital. Another feature of this Definitions of human capital have varied between an educated and study is that the density of human capital is included in the analysis, skilled workforce (Glaeser 1994, Glaeser & Saiz, 2004; Alquézar by measuring the number of both relative and absolute indicators. The Sabadie & Johansen, 2010) to talented and tolerant citizens (Florida, study demonstrates that the relationship between tolerance and inno- 2002). The tolerance aspect has received several criticisms (e.g. Clark, vation or human capital measures only holds for highly populated 2003; Glaeser, 2004) and has therefore been further redefined areas on the postal code level (Clark, 2003). Bringing together the bal- (Florida, 2012). The debate centres on whether firms locate in areas ance between share and volume, i.e. the relative and absolute level of with an educated and skilled workforce (Florida, 2002, 2012), or human capital, is also a new view point to studies concerning HMA. whether employees follow the firms (e.g. Scott, 2000, 2006). Earlier These have usually examined proportions, and therefore have conclud- studies have also recognised that human capital observed from sur- ed that detached housing areas with a high share (relative) of profes- rounding areas is more influential concerning innovation clustering sionals are more important than more densely populated areas than the area itself (Simonen & McCann, 2008). Therefore, the spatial in- hosting a higher absolute number of these professionals for innovation terdependence between human capital and innovative output is also driven development and economic growth (e.g. Vaattovaara, 1998; tested. This enables us to answer the
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